One informs the other: Unionid species at risk and benthic macroinvertebrate community monitoring data are complementary
Bibliographic record
Abstract
Benthic macroinvertebrate communities, which include unionid freshwater mussels, enhance the health of river ecosystems. Human impacts have driven declines within freshwater mussel communities and due to their complex life cycles, mussel recovery efforts are complex. In Canada, conservation of imperiled species has focused on biodiversity hotspots such as the Sydenham River in the Laurentian Great Lakes Basin. In practice, species conservation and habitat monitoring are siloed between federal agencies and local conservation authorities, limiting the potential for alignment of conservation policy and practice. Here we bring together federal, local, and our own survey data to explore patterns of co-occurrences between mussel species and other macroinvertebrate taxa to explore the extent to which knowledge of one benthic community informs the other. Mussel communities (species richness, community composition) differed between sites where imperiled mussel species were present and/or absent. Benthic macroinvertebrate metrics (e.g., family richness, percent Ephemeroptera, Plecoptera, and Trichoptera taxa) and specific indicator taxa were correlated with mussel species richness and the presence of imperiled mussel species. We show that benthic macroinvertebrate diversity indicators provided insight into imperiled species occurrences that warrant further investigation. These findings underscore support for coordinated watershed monitoring efforts and could be crucial for more successful freshwater mussel conservation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".